Edition: February/April 2018
ARTIFICIAL INTELLIGENCE (AI)
Overhyped or a revolution in asset management?
Hywel George, director of investments at Old Mutual Investment Group, offers an answer.
2017 saw the advent of the first fully AI-powered, daily traded exchange-traded funds. Some people view this as heralding a shift into a new investment paradigm, Autonomous Learning Investment Strategies (ALIS).
What’s new about these investment processes is that the technology learns and adapts as it goes along, based on the information and enormous data sets to which the algorithms have access. It’s on these that they base their investment decisions and solve problems, all without human input.
As in other AI fields, it has raised the spectre of ’singularity’ – a much-vaunted future state when computers could potentially have superintelligence that surpasses our own and which could, it is feared, ultimately put humans out of business. But have we truly crossed the AI Rubicon or is this merely hype?
For many, the AI milestones achieved over the past five years have set us up for the greatest technological revolution in history over the next decade. The investment industry will undoubtedly be at the centre of it. (See table).
But artificial intelligence and talk of technological revolution have been around for a while. For instance, in 1965 when a British mathematician and cryptologist brought up the concept of an intelligence explosion. Then, in 1993, a sci-fi writer and computer scientist predicted that within 30 years we would have the means to create superhuman intelligence.
There are many definitions of AI but Forbes magazine contributor David Thomas put it succinctly: Artificial intelligence is a branch of computer science that aims to create intelligent machines which teach themselves.
There are different levels of AI. Each level becomes more sophisticated and autonomous in the tasks computers can do without human intervention. There is machine learning (or structured learning) which is the ability of computers to learn and improve at tasks with experience. Then there is deep (or unstructured) learning, when a computer uses algorithms that adapt to new data and thus trains itself to perform tasks. The best-known examples of deep learning are IBM Watson and driverless cars. (See graphic).
A deeper understanding of AI
Inevitably, the advances in AI have spurred robust debate about what impact AI will have on the investment world. To get a balanced perspective, it’s worth considering why AI is developing so rapidly.
AI advances have primarily been made possible by the sharp decline in the price of graphics processing units (GPUs) in recent years. Driven by gaming, it has enabled AI to access immense amounts of data of all types (numerical, image, voice) being made available from companies such as Google, Facebook and Microsoft.
Cloud-based hosting has also provided access to extremely strong AI platforms. For instance, you can use IBM’s or Google’s AI platforms to take advantage of work that they have already done and build on top of this.
Why is this important?
Notwithstanding the increasingly fast-paced innovation we’ve seen, and the growing excitement about the potential of AI, it is not likely to be an investment panacea. It’s premature to think that fundamental qualitative investment professionals will no longer have jobs as a result of AI.
Instead, some of the things the investment industry needs to be thinking about are:
More important for the investment industry is to consider how can we use AI to improve portfolios and remove from our jobs the repetitive grudge aspects so that we can concentrate more time on the hard-thinking work; in other words, how we use AI to augment what we do as opposed to worrying about it replacing what we do.
This is not a matter of human versus machine but of human and machine being better than human alone.